MIE: A Medical Information Extractor towards Medical Dialogues | |
Yuanzhe Zhang1; Zhongtao Jiang1,2; Tao Zhang1,2; Shiwan Liu1,2; Jiarun Cao3; Kang Liu1,2; Shengping Liu4; Jun Zhao1,2 | |
2020-07 | |
会议日期 | July 5, 2020 - July 10, 2020 |
会议地点 | Online |
英文摘要 | Electronic Medical Records (EMRs) have become key components of modern medical care systems. Despite the merits of EMRs, many doctors suffer from writing them, which is time-consuming and tedious. We believe that automatically converting medical dialogues to EMRs can greatly reduce the burdens of doctors, and extracting information from medical dialogues is an essential step. To this end, we annotate online medical consultation dialogues in a window-sliding style, which is much easier than the sequential labeling annotation. We then propose a Medical Information Extractor (MIE) towards medical dialogues. MIE is able to extract mentioned symptoms, surgeries, tests, other information and their corresponding status. To tackle the particular challenges of the task, MIE uses a deep matching architecture, taking dialogue turn-interaction into account. The experimental results demonstrate MIE is a promising solution to extract medical information from doctor-patient dialogues. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/40582] |
专题 | 模式识别国家重点实验室_自然语言处理 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.National Centre for Text Mining, University of Manchester 4.Beijing Unisound Information Technology Co., Ltd |
推荐引用方式 GB/T 7714 | Yuanzhe Zhang,Zhongtao Jiang,Tao Zhang,et al. MIE: A Medical Information Extractor towards Medical Dialogues[C]. 见:. Online. July 5, 2020 - July 10, 2020. |
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